Загрузка видео...

Не удалось загрузить видео

На главную

I'm starting a curated list of interactive machine learning demos: Looking for more suggestions! My plan is to incorporate some into the ML modules of Cambridge's new MPhil in Data Intensive Science, as a way to hone students' intuition.

139,459 просмотров • 2 лет назад •via X (Twitter)

Комментарии: 10

Фото профиля Miles Cranmer
Miles Cranmer2 лет назад

I feel like you may know of some good demos out there... Any tips appreciated!

Фото профиля Anthony Gitter
Anthony Gitter2 лет назад

@PoloChau has a lot of good ones like CNN explainer

Фото профиля Thomas Coudrat
Thomas Coudrat2 лет назад

@sonmchems Someone had already started a good list:

Фото профиля Nils
Nils2 лет назад

Distill also has additional visualizations

Фото профиля Zeel B Patel
Zeel B Patel2 лет назад

and have great peer reviewed interactive ML demos! Some more: - Bayes Opt- - GPs - - Visual proof that NNs can approximate any function -

Фото профиля GAMA Miguel Angel 🐦‍⬛🔑
GAMA Miguel Angel 🐦‍⬛🔑2 лет назад

Maybe this couple of webpages with many awesome demos :) Many algorithms: Probability theory:

Фото профиля Harshang
Harshang2 лет назад

I've been working on a gradient descent demo. It's not much, but putting it forward as a candidate. All suggestions are welcome.

Фото профиля Kartik C
Kartik C2 лет назад

Awesome initiative!! these are straight from my bookmarks: 1)A similar initiative 2)Intro to Vector Math 3)Cool in browser game 4)Intro to Fourier Transforms (which are also universal function appxmtrs)

Фото профиля Kartik C
Kartik C2 лет назад

another short list: Linear Algebra Immersive Book: Learning Graph theory: Intro to Markov Chains: Many gems here too:

Фото профиля Yong-Hyun Park
Yong-Hyun Park2 лет назад

It beautifully visualizes the transformation in 2-dim NN.

Похожие видео

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

ℏεsam

117,570 просмотров • 1 год назад

if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

ℏεsam

109,292 просмотров • 1 год назад